Testing Scales: In the inference stage, various testing sizes of the input image can obviously affect the performance and speed of the model, as displayed in Table REF  and Table REF . For example, in PAN [1]} that does not pre-train on SynText, with the increase of the short size of the input image, the \(\textit {F-measure}\)  increases by 3.8% and 5.1% on the datasets CTW and TOT, respectively. Moreover, some methods even use the multi-scale testing strategy to promote the performance. However, they may only have a slight improvement on performance but consequently bring the decrease in speed. For example, the recent work CRNet [2]} with the multi-scale testing strategy (Table REF ) only increases 0.3% in \(\textit {F-measure}\) , compared with the previous method ContourNet [3]} using the single-scale testing strategy.
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